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Record W2800892855 · doi:10.7939/r3ff3m53n

Wicked Problems and Professional Work: Disrupting Work in a Mature Field with Incumbent Professions

2015· article· en· W2800892855 on OpenAlexaboutno aff
Jo-Louise Huq

Bibliographic record

VenueUniversity of Alberta Library · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Field (mathematics)Engineering ethicsBoundary-workSociologyPublic relationsPolitical scienceEngineeringSocial science

Abstract

fetched live from OpenAlex

The emerging institutional work perspective implicates agency and action in institutional dynamics in contrast to the traditional organizational institutional approach. In this dissertation, my objective was to explore institutional disrupting work in a mature field with incumbent professions. I developed a two-part case study of the Alberta addictions treatment field. The first part of the case study explored disrupting work at the field level, the second part at the level of professional practice. Data collection included interviews, observations, and documents. My findings show that at the field level and at the level of practice actors’ actions were disrupting to institutionalized arrangements and practices of professional work. At the field level, I identified three forms of disrupting work: complexifying work, boundary work, and temporal work. At the practice level, I also identified three forms of disrupting work: configuring work, adapting work, and boundary work. I developed three models of disrupting a field level model, a practice level model, and a multi-level model. My research sheds light on disrupting and how disrupting interrupts the institutionalized arrangements and practices of incumbent professions, both of which, despite scholars’ interest in action and agency in institutional life, remain overlooked in empirical research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.177
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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